Introduction to Computing -CS101

Alan Turing's Automatic Computing Engine, 2005

The Turing test is considered a test for human-like intelligence in a computer. Some debate exists as to whether the test it too strong, too weak, or too narrow (it tests only conversational behavior). But that it tests conversational capacity is not disputed. I argue that this view is mistaken. As performed, the test is incapable of indicating any type of machine intelligence, no matter what responses the computer contestant delivers to the judge. This failure reveals that computation provides a flawed understanding of the computer, and indicates where a more useful conception of the machine might be found.

eCAADe proceedings

The Turing test: the elusive …, 2003

This eagerly awaited anthology, while surely not the last word on the Turing Test, equally surely deserves to become the principal source of information on the test. It includes not only Turing's classic paper, but a fine selection of the main replies to date, all tied together by an engaging and penetrating essay by the editor.

Encyclopedia of Language & Linguistics, 2006

This document is a draft of an article for the Encyclopedia of Language and Linguistics, 2nd Edition (Elsevier, forthcoming). This article describes the Turing Test for determining whether a computer can think. It begins with a description of an "imitation game" for discriminating between a man and a woman, discusses variations of the Test, standards for passing the Test, and experiments with real Turing-like tests (including Eliza and the Loebner competition). It then considers what a 1 computer must be able to do in order to pass a Turing Test, including whether written linguistic behavior is a reasonable replacement for "cognition", what counts as understanding natural language, the role of world knowledge in understanding natural language, and the philosophical implications of passing a Turing Test, including whether passing is a sufficient demonstration of cognition, briefly discussing two counterexamples: a table-lookup program and the Chinese Room Argument.

2019

"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." (Edsger W. Dijkstra) 1 Artificial Intelligence n.-the theory and development of computer systems able to perform tasks that normally require human ABSTRACT Technology, innovation, and artificial intelligence have advanced drastically in the past few decades. According to one philosopher, Lady Lovelace, she argued that, A computer cannot "originate anything" but only "can do whatever we know how to order it to perform". But Allan Turing proved that his "universal computing machine" would be capable of performing any conceivable mathematical computation if it were representable as an algorithm. I will argue that an action made by an artefact does not only depend on the order made by its human designer rather an artefact is capable of learning.

2016

Can machines be intelligent? In his 1950 Mind article, Alan Turing introduced the Imitation Game in which a machine tries to imitate human intellectual behavior to such an extent that a human interrogator mistakes the machine for a human. The Imitation Game, also known as the "Turing Test," has been commonly understood to be a test that determines if a machine is intelligent. According to the accepted reading, Turing was of the opinion that a machine that performs well in the game is intelligent.

Convergence: The International Journal of Research into New Media Technologies

This article discusses the role of technological myths in the development of artificial intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: The creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on AI published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers and technologists, three dominant patterns in the construction of the AI myth are identified: (1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies; (2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome and (3) the relevance of controversies around the claims of AI, which we argue...

IEEE Annals of the History of Computing

This article presents an overview of Turing's early contributions to machine intelligence, together with a summary of his influence on other early practitioners. Following his famous work on the Entscheidungsproblem in the 1930s, Turing staked out the field of machine intelligence during the 1940s. His wartime Bombe used what we now call heuristic search to do work requiring intelligence when done by humans. In key papers in 1948 and 1950 he discussed search, learning, robotics, chess, the theorem-proving approach to AI, the genetic algorithm concept, and artificial neural networks, as well as introducing the Turing test. He influenced the first generation of programmers in Britain, whose pioneering contributions to machine intelligence included work on board games, learning, language-processing, and reasoningcontributions made years before the term 'Artificial Intelligence' was coined at Dartmouth in 1956. Context: Thinking Machines, Dartmouth, the Turing Machine, and the Entscheidungsproblem Tracing far back along the chain of intellectual precursors to modern AI, one reaches the American philosopher C. S. Peirce. In 1908, Peirce expressed the idea that a machine-'some Babbage's analytical engine or some logical machine'-is capable of all mathematical reasoning [47, p. 434]. He was skeptical, however, calling the idea 'malignant', and firmly placing it among others he deemed 'logical heresies' (ibid.) Peirce was a powerful influence on Hilbert and his group at Göttingen [27, p. 1]. In 1903, Peirce formulated the decision problem for first-order logic in roughly the form in which Turing later tackled it [46]. At Göttingen the decision problem was named the Entscheidungsproblem, it seems by Behmann, a young member of Hilbert's group [34]. As early as 1921, Behmann used the concept of a machine to clarify the nature of the Entscheidungsproblem, saying: 'One might, if one wanted to, speak of mechanical or machine-like thinking' and 'Perhaps one can one day even let it be carried out by a machine' [34, p. 176]. From Göttingen, the Entscheidungsproblem travelled to Cambridge and Turing. His encounter with the Entscheidungsproblem launched him on the trajectory that led to his theoretical work on what he called 'logical computing machines' and then, eventually, to his practical work on the hardware and software designs for the ACE and other early British electronic computers [9, 18, 29, 30]. This same trajectory issued in his early work on what he called 'intelligent machinery'. Researchers that he influenced termed the new field machine intelligence.